Abstract

Even though robotic rehabilitation is very useful to improve motor function, there is no conclusive evidence on its role in reducing post-stroke spasticity. Focal muscle vibration (MV) is instead very useful to reduce segmental spasticity, with a consequent positive effect on motor function. Therefore, it could be possible to strengthen the effects of robotic rehabilitation by coupling MV. To this end, we designed a pilot randomized controlled trial (Clinical Trial NCT03110718) that included twenty patients suffering from unilateral post-stroke upper limb spasticity. Patients underwent 40 daily sessions of Armeo-Power training (1 hour/session, 5 sessions/week, for 8 weeks) with or without spastic antagonist MV. They were randomized into two groups of 10 individuals, which received (group-A) or not (group-B) MV. The intensity of MV, represented by the peak acceleration (a-peak), was calculated by the formula (2πf)2A, where f is the frequency of MV and A is the amplitude. Modified Ashworth Scale (MAS), short intracortical inhibition (SICI), and Hmax/Mmax ratio (HMR) were the primary outcomes measured before and after (immediately and 4 weeks later) the end of the treatment. In all patients of group-A, we observed a greater reduction of MAS (p = 0.007, d = 0.6) and HMR (p<0.001, d = 0.7), and a more evident increase of SICI (p<0.001, d = 0.7) up to 4 weeks after the end of the treatment, as compared to group-B. Likewise, group-A showed a greater function outcome of upper limb (Functional Independence Measure p = 0.1, d = 0.7; Fugl-Meyer Assessment of the Upper Extremity p = 0.007, d = 0.4) up to 4 weeks after the end of the treatment. A significant correlation was found between the degree of MAS reduction and SICI increase in the agonist spastic muscles (p = 0.004). Our data show that this combined rehabilitative approach could be a promising option in improving upper limb spasticity and motor function. We could hypothesize that the greater rehabilitative outcome improvement may depend on a reshape of corticospinal plasticity induced by a sort of associative plasticity between Armeo-Power and MV.

Introduction

Spasticity is defined as a velocity-dependent increase in muscle tone due to the hyper-excitability of muscle stretch reflex [1]. Spasticity of the upper limb is a common condition following stroke and traumatic brain injury and needs to be assessed carefully because of the significant adverse effects on patient’s motor functions, autonomy, and quality of life [2].

Different pharmacological and non-pharmacological approaches are currently available for upper limb spasticity management, as physiotherapy (including magnetic stimulation, electromagnetic therapy, sensory-motor techniques, and functional electrical stimulation treatment) and robot-assisted therapy [3–4]. In this regard, several studies suggest robotic devices, including the Armeo® (a robotic exoskeleton for the rehabilitation of upper limbs), may help reducing spasticity by modifying spasticity-related synaptic processes at either the brain or spinal level [5–13], resulting in spasticity reduction in antagonist muscles through, e.g., a strengthening of spinal reciprocal inhibition mechanisms [11].

Growing research is proposing segmental muscle vibration (MV) as being a powerful tool for the treatment of focal spasticity in post-stroke patients [14–15]. Mechanical devices deliver low-amplitude/high-frequency vibratory stimuli to specific muscles [16–17], thus offering strong proprioceptive inputs by activating the neural pathway from muscle spindle annulospiral endings to Ia-fiber, dorsal column–medial lemniscal pathway, the ventral posterolateral nucleus of the thalamus (and other nuclei of the basal ganglia), up to the primary somatosensory area (postcentral gyrus and posterior paracentral lobule of the parietal lobe), and the primary motor cortex [18–19]. At the cortical network level, proprioceptive inputs can alter the excitability of the corticospinal pathway by modulating intracortical inhibitory and facilitatory networks within primary sensory and motor cortex, and affecting the strength of sensory inputs to motor circuits [20–22]. In particular, periods of focal MV delivered alone can modify sensorimotor organization within the primary motor cortex (i.e., can increase or decrease motor evoked potential—MEP—and short intracortical inhibition (SICI) magnitude in the vibrated muscles, while opposite changes occur in the neighboring muscles), thus reducing segmental hyper-excitability and spasticity [20–22].

While focal MV is commonly used to reduce upper limb post-stroke spasticity, there is no conclusive evidence on the role of robotic rehabilitation in such a condition [14–17,23–27]. A strengthening of the effects of neurorobotics and MV on spasticity could be achieved by combining MV and neurorobotics. The rationale for combining Armeo-Power and MV to reduce spasticity could lie in the summation and amplification of their single modulatory effects on corticospinal excitability [28]. Specifically, it is hypothesizable that MV may strengthen the learning-dependent plasticity processes within sensory-motor areas that are in turn triggered by the intensive, repetitive, and task-oriented movement training offered by Armeo-Power [29–30]. Such an amplification may depend on a sort of associative plasticity (i.e., the one generated by timely coupling two different synaptic inputs) between MV and Armeo-Power [31–33].

To the best of our knowledge, this is the first attempt to investigate such approach. Indeed, a previous study combining MV with conventional physiotherapy used Armeo only as evaluating tool [14].

The aim of our study was to assess whether a combined protocol employing MV and Armeo-Power training, as compared to Armeo-Power alone, may improve upper limb spasticity and motor function in patients suffering from a hemispheric stroke in the chronic phase. To this end, we compared the clinical and electrophysiological after-effects of Armeo-Power with or without MV on upper limb spasticity. We also assessed the effects on upper limb motor function and muscle activation, disability burden, and mood, given that spasticity may have significant negative consequences on these outcomes. Further, it is important to evaluate mood, as it may negatively affect functional recovery [34–36], increase mortality [37], and weaken the compliance of the patient to the rehabilitative training [38–39].[…]

Abstract

This paper presents the design process of an exoskeleton for executing human fingers’ extension movement for the rehabilitation procedures and as an active orthosis purposes. The Fingers Extending eXoskeleton (FEX) is a serial, under-actuated mechanism capable of executing fingers’ extension. The proposed solution is easily adaptable to any finger length or position of the joints. FEX is based on the state-of-art FingerSpine serial system. Straightening force is transmitted from a DC motor to the exoskeleton structures with use of pulled tendons. In trial tests the device showed good usability and functionality. The final prototype is a result of almost half a year of the development process described in this paper.

Wege A, Hommel G (2005) Development and control of a hand exoskeleton for rehabilitation of hand injuries. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Berlin, Germany, pp 3461–3466Google Scholar

Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of 7 major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested 6 movement directions and 4 force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

Introduction

Despite their participation in standard rehabilitation programs (Jørgensen et al., 1999; Dobkin, 2005), restoration of arm and hand function for activities of daily living is not achieved in the majority of stroke patients. In the first weeks and months after stroke, a positive relationship between the dose of therapy and clinically meaningful improvements has been demonstrated (Lohse et al., 2014; Pollock et al., 2014). In stroke patients with long-standing (>6 months) upper limb paresis, however, treatment effects were small, with no evidence of a dose-response effect of task-specific training on the functional capacity (Lang et al., 2016). This has implications for the use of assistive technologies such as robot-assisted training during stroke rehabilitation. These devices are usually applied to further increase and standardize the amount of therapy. They have the potential to improve arm/hand function and muscle strength, albeit currently available clinical trials provide on the whole only low-quality evidence (Mehrholz et al., 2015). It has, notably, been suggested that technology-assisted improvements during stroke rehabilitation might at least partially be due to unspecific influences such as increased enthusiasm for novel interventions on the part of both patients and therapists (Kwakkel and Meskers, 2014). In particular, a comparison between robot-assisted training and dose-matched conventional physiotherapy in controlled trials revealed no additional, clinically relevant benefits (Lo et al., 2010; Klamroth-Marganska et al., 2014). This might be related to saturation effects. Alternatively, the active robotic assistance might be too supportive when providing “assistance-as-needed” during the exercises (Chase, 2014). More targeted assistance might therefore be necessary during these rehabilitation exercises to maintain engagement without compromising the patients’ motivation; i.e., by providing only as much support as necessary and as little as possible (Grimm and Gharabaghi, 2016). In this context, passive gravity compensation with a multi-joint arm exoskeleton may be a viable alternative to active robotic assistance (Housman et al., 2009; Grimm et al., 2016a). In severely affected patients, performance-dependent, neuromuscular electrical stimulation of individual upper limb muscles integrated in the exoskeleton may increase the range of motion even further (Grimm and Gharabaghi, 2016; Grimm et al., 2016b). These approaches focus on the improvement of motor control, which is defined as the ability to make accurate and precise goal-directed movements without reducing movement speed (Reis et al., 2009; Shmuelof et al., 2012), or using compensatory movements (Kitago et al., 2013, 2015). Functional gains in hemiparetic patients, however, are often achieved by movements that aim to compensate the diminished range of motion of the affected limb (Cirstea and Levin, 2000; Grimm et al., 2016a). Although these compensatory strategies might be efficient in short-term task accomplishment, they may lead to long-term complications such as pain and joint-contracture (Cirstea and Levin, 2007; Grimm et al., 2016a). In this context, providing detailed information about how the movement is carried out, i.e., the quality of the movement, is more likely to recover natural movement patterns and avoid compensatory movements, than to provide information about movement outcome only (Cirstea et al., 2006; Cirstea and Levin, 2007; Grimm et al., 2016a). This feedback, however, needs to be provided implicitly, since explicit information has been shown to disrupt motor learning in stroke patients (Boyd and Winstein, 2004, 2006; Cirstea and Levin, 2007). Information on movement quality has therefore been incorporated as implicit closed-loop feedback in the virtual environment of an exoskeleton-based rehabilitation device (Grimm et al., 2016a). Specifically, the continuous visual feedback of the whole arm kinematics allowed the patients to adjust their movement quality online during each task; an approach closely resembling natural motor learning (Grimm et al., 2016a).

Along these lines, virtual reality and interactive video gaming have emerged as treatment approaches in stroke rehabilitation (Laver et al., 2015). They have been used as an adjunct to conventional care (to increase overall therapy time) or compared with the same dose of conventional therapy. These studies have demonstrated benefits in improving upper limb function and activities of daily living, albeit currently available clinical trials tend to provide only low-quality evidence (Laver et al., 2015). Most of these studies were conducted with mildly to moderately affected patients. In the remaining patient group with moderate to severe upper limp impairment, the intervention effects were more heterogeneous and affected by the impairment level, with either no or only modest additional gains in comparison to dose-matched conventional treatments (Housman et al., 2009; Byl et al., 2013; Subramanian et al., 2013).

With respect to the restoration of arm and hand function in severely affected stroke patients in particular, there is still a lack of evidence for additional benefits from technology-assisted interventions for activities of daily living. The only means of providing such evidence is by sufficiently powered, randomized and adequately controlled trials (RCT).

However, such high-quality RCT studies require considerable resources. Pilot data acquired earlier in the course of feasibility studies may provide the rationale and justification for later large-scale RCT. Such studies therefore need to demonstrate significant improvements, with functional relevance for the participating patients. Then again, costly RCT can be avoided when innovative interventions prove to be feasible but not effective with regard to the treatment goal, i.e., that they do not result in functionally relevant upper extremity improvements in severely affected stroke patients.

One recent pilot study, for example, applied brain signals to control an active robotic exoskeleton within the framework of a brain-robot interface (BRI) for stroke rehabilitation. This device provided patient control over the training device via motor imagery-related oscillations of the ipsilesional cortex (Brauchle et al., 2015). The study illustrated that a BRI may successfully link three-dimensional robotic training to the participant’s effort. Furthermore, the BRI allowed the severely impaired stroke patients to perform task-oriented activities with a physiologically controlled multi-joint exoskeleton. However, this approach did not result in significant upper limb improvements with functional relevance for the participating patients. This training approach was potentially too challenging and may even have frustrated the patients (Fels et al., 2015). The patients’ cognitive resources for coping with the mental load of performing such a neurofeedback task must therefore be taken into consideration (Bauer and Gharabaghi, 2015a; Naros and Gharabaghi, 2015). Mathematical modeling on the basis of Bayesian simulation indicates that this might be achieved when the task difficulty is adapted in the course of the training (Bauer and Gharabaghi, 2015b). Such an adaptation strategy has the potential to facilitate reinforcement learning (Naros et al., 2016b) by progressively challenging the patient (Naros and Gharabaghi, 2015). Recent studies explored automated adaptation of training difficulty in stroke rehabilitation of less severely affected patients (Metzger et al., 2014; Wittmann et al., 2015). More specifically, both robot-assisted rehabilitation of proprioceptive hand function (Metzger et al., 2014) and inertial sensor-based virtual reality feedback of the arm (Wittmann et al., 2015) benefit from assessment-driven adjustments of exercise difficulty. Furthermore, a direct comparison between adaptive BRI training and non-adaptive training (Naros et al., 2016b) or sham adaptation (Bauer et al., 2016a) in healthy patients revealed the impact of reinforcement-based adaptation for the improvement of performance. Moreover, the exercise difficulty has been shown to influence the learning incentive during the training; more specifically, the optimal difficulty level could be determined empirically while disentangling the relative contribution of neurofeedback specificity and sensitivity (Bauer et al., 2016b).

In the present 4-week pilot study, we combined these approaches and customized them for the requirements of patients with severe upper extremity impairment by applying a multi-joint exoskeleton for task-oriented arm and hand training in an adaptive virtual environment. Notably, due to the severity of their impairment, these patients were not able to practice the reach-to-grasp movements without the exoskeleton. The set-up was, however, limited to pure antigravity support, i.e., it provided passive rather than active assistance. Furthermore, it tested the feasibility of closed-loop online adaptation of exercise difficulty and aimed at automated progression of task challenge.

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

Abstract

BACKGROUND: Robotic rehabilitation is promising to promote function in stroke patients. The assist as needed training paradigm has shown to stimulate neuroplasticity but often cannot be used because stroke patients are too impaired to actively control the robot against gravity.

AIM: To verify whether a rehabilitation intervention based on robot fully assisted Reaching against gravity (RCH) and Hand-to-Mouth (HTM) can promote upper-limb function in chronic stroke.

DESIGN: Cohort study.

METHODS: Patients underwent 12 sessions (3 per week) of robotic treatment using an end- effector robot Every session consisted of 20 minutes each of RCH and HtM; movements were fully assisted, but patients were asked to try to actively participate. The Fugl-Meyer Assessment (FMA) was the primary outcome measure; Medical Research Council and Modified Ashworth Scale were the secondary outcome measures.

CONCLUSION: This preliminary study shows that a robotic intervention based on functional movements, fully assisted, can be effective in promoting function in chronic stroke patients. These results are promising considering the short time of the intervention (1 month) and the time from the stroke event, which was large (27±20 months). A larger study, comprehensive of objective instrumental measures, is necessary to confirm the results.

CLINICAL REHABILITATION IMPACT: This intervention could be extended even to subacute stroke and other neurological disorders.

Abstract

Background

Common scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories. However, there is no a general consensus on which parameters to use. Moreover, the selected variables may be redundant and highly correlated or, conversely, may incompletely sample the kinematic information from the trajectories. Here we sought to identify a set of clinically useful variables for an exhaustive but yet economical kinematic characterization of upper limb movements performed by post-stroke hemiparetic subjects.

Methods

For this purpose, we pursued a top-down model-driven approach, seeking which kinematic parameters were pivotal for a computational model to generate trajectories of point-to-point planar movements similar to those made by post-stroke subjects at different levels of impairment.

Results

The set of kinematic variables used in the model allowed for the generation of trajectories significantly similar to those of either sub-acute or chronic post-stroke patients at different time points during the therapy. Simulated trajectories also correctly reproduced many kinematic features of real movements, as assessed by an extensive set of kinematic metrics computed on both real and simulated curves. When inspected for redundancy, we found that variations in the variables used in the model were explained by three different underlying and unobserved factors related to movement efficiency, speed, and accuracy, possibly revealing different working mechanisms of recovery.

Conclusion

This study identified a set of measures capable of extensively characterizing the kinematics of upper limb movements performed by post-stroke subjects and of tracking changes of different motor improvement aspects throughout the rehabilitation process.

Background

Upper limb functions are altered in about 80 % of acute stroke survivors and in about 50 % of chronic post-stroke patients [1]. With the increasing of life expectancy, it has been estimated that stroke related impairments will be ranked to the fourth most important causes of adult disability in 2030 [2], prompting the need to design more effective diagnostic and rehabilitative tools [3, 4].

Together with more traditional and widely accepted clinical scales in the last two decades investigators have characterized post-stroke motor recovery also in terms of kinematic parameters extracted from hand and arm task-oriented movements [3, 5], which offer more objective measures of motor performance [6]. Indeed, clinical scales, whose reliability has often been questioned [7, 8, 9], may not be sensitive to small and more specific changes [10] and could be of limited use to distinguish different aspects of motor improvement [11, 12].

Previous robot-assisted clinical and pilot studies have proposed a large set of kinematic parameters to characterize motor improvements [5, 6, 11]. A few of them focused on finding a significant relationship between robotic measures collected longitudinally in post-stroke patients and clinical outcome measures, to increase acceptance of kinematic evaluation scales in practice [5, 6]. Too little effort, however, has been made to identify the different aspects of movement improvement, how they can be described by kinematic robot-based measures [11], and whether they may dissociate with respect to recovery time course and to training response [11].

Indeed the range of potential changes in limb trajectory during recovery is not known a priori [12] and might not be fully represented by a set of arbitrarily selected parameters extracted from limb trajectories, even if the parameters were chosen according to a certain number of study hypotheses or to significant relationships with clinical scales. Moreover, these variables can be highly correlated and, thus, redundant. Although redundancy can be tackled by data reduction algorithms, such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) [5, 6], incomplete representation of information might still remain an overlooked issue.

In the present study we aimed at devising a novel method for identifying a set of kinematic measures potentially capable of fully highlighting and tracking changes of different aspects of movement performance throughout the rehabilitation training. Instead of starting from a certain number of a priori hypotheses, we sought to find which variables were essential for modeling trajectories of post-stroke patients and were, thus, informative of kinematic features of upper limb movements. We then tested whether the identified kinematic parameters i) were capable of highlighting changes in movement performance, ii) were to some extent redundant, and iii) were informative of different factors of post-stroke motor impairment, such as paresis, loss of fractionated movement and somatosensation, and abnormal muscle tone [13].

Fig. 1 Schematic overview of the computational model for post-stroke trajectories simulation. (1) Endpoint kinematics of one pathological subject making point to point forward and backward movements from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius assisted by InMotion2. (2) Kinematic parameters are extracted from the real trajectories of the post-stroke subjects. The tangential speed profile of real trajectories (for each movement direction and subject, separately for each group of patients and time of therapy) is analyzed to extract probability distributions of nPK, <σ>, T, and MV. (3) Based on the inferred probability distributions, tangential speed profiles of simulated trajectories are generated by solving a constrained optimization problem (Eq. 2). (4) The transversal and longitudinal speed profiles of real trajectories are analyzed to extract probability distributions of ratio-amp L , ratio-amp N , ratio-nPK, MV N , CONT L , and CONT N . (5) From the simulated tangential velocities and the inferred distributions of kinematic parameters, transversal and longitudinal speed profiles of simulated trajectories are generated, by solving an unconstrained optimization problem (Eq. 4). (6) The trajectories in the Cartesian space defined by the (L, N) axes (see step 4) are obtained from the speed profiles by numerical integration. (7) The generated trajectories are then rotated by means of a geometrical transformation to reproduce the point-to-point movements performed by post-stroke subjects during a turn in the InMotion2 coordinate frame system

[Purpose] The purpose of this case series was to determine the effects of robot-assisted hand rehabilitation with a Gloreha device on skeletal muscle perfusion, spasticity, and motor function in subjects with poststroke hemiparesis.

[Subjects and Methods] Seven patients, 2 women and 5 men (mean ± SD age: 60.5 ±6.3 years), with hemiparesis (>6 months poststroke), received passive mobilization of the hand with a Gloreha (Idrogenet, Italy), device (30 min per day; 3 sessions a week for 3 weeks). The outcome measures were the total hemoglobin profiles and tissue oxygenation index (TOI) in the muscle tissue evaluated through near-infrared spectroscopy. The Motricity Index and modified Ashworth Scale for upper limb muscles were used to assess mobility of the upper extremity.

[Results] Robotic assistance reduced spasticity after the intervention by 68.6% in the upper limb. The Motricity Index was unchanged in these patients after treatment. Regarding changes in muscle perfusion, significant improvements were found in total hemoglobin. There were significant differences between the pre- and posttreatment modified Ashworth scale.

[Conclusion] The present work provides novel evidence that robotic assistance of the hand induced changes in local muscle blood flow and oxygen supply, diminished spasticity, and decreased subject-reported symptoms of heaviness and stiffness in subjects with post-stroke hemiparesis.

Abstract

Background

The combination of robot-assisted therapy (RT) and a modified form of constraint-induced therapy (mCIT) shows promise for improving motor function of patients with stroke. However, whether the changes of motor control strategies are concomitant with the improvements in motor function after combination of RT and mCIT (RT + mCIT) is unclear. This study investigated the effects of the sequential combination of RT + mCIT compared with RT alone on the strategies of motor control measured by kinematic analysis and on motor function and daily performance measured by clinical scales.

Methods

The study enrolled 34 patients with chronic stroke. The data were derived from part of a single-blinded randomized controlled trial. Participants in the RT + mCIT and RT groups received 20 therapy sessions (90 to 105 min/day, 5 days for 4 weeks). Patients in the RT + mCIT group received 10 RT sessions for first 2 weeks and 10 mCIT sessions for the next 2 weeks. The Bi-Manu-Track was used in RT sessions to provide bilateral practice of wrist and forearm movements. The primary outcome was kinematic variables in a task of reaching to press a desk bell. Secondary outcomes included scores on the Wolf Motor Function Test, Functional Independence Measure, and Nottingham Extended Activities of Daily Living. All outcome measures were administered before and after intervention.

Results

RT + mCIT and RT demonstrated different benefits on motor control strategies. RT + mCIT uniquely improved motor control strategies by reducing shoulder abduction, increasing elbow extension, and decreasing trunk compensatory movement during the reaching task. Motor function and quality of the affected limb was improved, and patients achieved greater independence in instrumental activities of daily living. Force generation at movement initiation was improved in the patients who received RT.

Conclusion

A combination of RT and mCIT could be an effective approach to improve stroke rehabilitation outcomes, achieving better motor control strategies, motor function, and functional independence of instrumental activities of daily living.

Trial registration

Background

Stroke remains a leading cause of permanent motor disability worldwide [1]. Persistent impairment of the upper extremity (UE) occurs in up to two-thirds of patients after stroke [2]. UE paresis can lead to deficits in motor control [3], motor dysfunction [4], and participation in activities of daily living (ADL) [5]. Developing and providing effective therapeutic techniques to improve UE motor control and recovery is crucial.

Robot-assisted therapy (RT) is an emerging intervention approach that provides high-intensity, high-repetition, and task-specific training to enhance motor learning and control in patients with stroke [6, 7]. Systemic reviews have indicated that RT improves UE muscle strength and motor function of patients with moderate to severe motor impairment after stroke [8,9]. A recent review suggested that the assessment of movement kinematics should be included in RT studies to identify modulation in motor control strategies [10]. Previous studies found that RT can improve motor control strategies in patients with stroke, including greater movement efficacy [11–13], better movement smoothness of the affected UE [13], and more use of the preplanned control strategy [13]. However, no consistent findings on patients’ participation in ADL were observed after RT [8, 14–17]. How to optimize or transfer the treatment benefits of RT on motor function and motor control strategies into participation in ADL warrants further investigation. An approach using RT monotherapy may not optimally address this need.

Constraint-induced therapy (CIT), one most investigated approaches to rehabilitation, was developed to overcome the learned nonuse phenomenon and enhance functional use of the affected arm after stroke [18, 19]. Treatment components of CIT include repetitive and intensive task practice, behavioral shaping techniques, restraint of the unaffected UE, and transfer package [20, 21]. Modified and distributed CIT, which are not as intensive as the original CIT, have been developed and validated [20, 22, 23]. The benefits of the original CIT and its modified versions have been well demonstrated to improve motor function, arm-hand activities, and daily performance of patients with stroke [19, 24, 25].

Therapies that combine RT with other rehabilitation approaches have been developed to optimize the treatment effects of RT [26–29]. The combination of RT and conventional therapy led to significant gains in arm function of patients, but different combination sequences showed benefits in different outcomes [27]. In addition, RT combined with repetitive task practice was effective in enhancing hand function and stroke recovery of patients [28]. To the best of our knowledge, only one study has investigated the treatment effects of sequencing the combination of RT and a modified form of CIT (mCIT) in patients with stroke [29]. The results indicated that the sequential combination of RT and mCIT led to better motor and functional ability measured by clinical scales compared with RT alone or conventional rehabilitation [29]. However, whether the changes in motor control strategies are responsible for the improvements in motor function after the sequential combination therapy remains unclear.

Kinematic analysis has been recommended as a sound measure to provide objective and sensitive evaluations on spatial and temporal characteristics of UE movements [8]. More importantly, kinematics can capture motor control strategies that cannot be detected by clinical scales [30]. Thus, kinematic analysis enables us to understand whether the behavioral improvement is due to a true change in the end point control and joint motion or is a result of compensation. Kinematic measures, along with clinical assessments, can better clarify the motor control strategies underlying the motor improvements of stroke patients [31, 32].

This study investigated the effects of the sequential combination of RT and mCIT (RT + mCIT), compared with RT alone, focusing on motor control strategies measured by kinematic analysis and on motor and ADL functions using clinical measures. We hypothesized that (1) RT + mCIT would lead to different benefits on the motor control strategies compared with and RT alone and that (2) RT + mCIT would contribute to better performances in ADL than RT alone.

Gait abnormalities following neurological disorders are often disabling, negatively affecting patients’ quality of life. Therefore, regaining of walking is considered one of the primary objectives of the rehabilitation process. To overcome problems related to conventional physical therapy, in the last years there has been an intense technological development of robotic devices, and robotic rehabilitation has proved to play a major role in improving one’s ability to walk.

The robotic rehabilitation systems can be classified into stationary and overground walking systems, and several studies have demonstrated their usefulness in patients after severe acquired brain injury, spinal cord injury and other neurological diseases, including Parkinson’s disease, multiple sclerosis and cerebral palsy.

In this review, we want to highlight which are the most widely used devices today for gait neurological rehabilitation, focusing on their functioning, effectiveness and challenges.

Novel and promising rehabilitation tools, including the use of virtual reality, are also discussed.